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Dynsf

Dynsf is a simple tool for calculaton partial dynamical structure factors from molecular dynamics (MD) simulations.

The main input consists of a trajectory output from a MD simulation; i.e. a file containing snapshots of the particle coordinates, and optionally velocities, for consecutively and equally spaced points in (simulation) time.

Dynsf can by itself read and parse standard lammpsdump-style trajectories. If libgmx (gromacs lib) is available, dynsf can use it to read gromacs xtc-files. If VMD is available, dynsf can use VMD's molfileplugin to read other formats (with some limitations) as well.

Dynsf is controlled via command line options, provided at command invokation. The main difficulties in using dynsf lies in deciding which particular values to use.

Output can be written in standard matlab/octave-stype .m-files, and/or as python pickle-files.

The most time critical part of the processing consists of the calculation of the fourier transformed densities, rho(k). This part is implemented in C, and is greatly improved by parallelizing it using OpenMP or OpenACC. The time correlation calculation is performed explicitly in python (using numpy arrays), and is parallelized with python threads (which works rather well thanks to numpy releasing the python GIL).

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Installation

Requirements

Python 2.6/2.7, numpy, and a C99-complient C-compiler.

Quickstart

Decide a installation prefix (e.g. $HOME/opt/ or such)
Install, "python ./setup.py install --prefix=$HOME/opt"
Set PYTHONPATH to include newly installed site-packages directory
(e.g. $HOME/opt/lib64/python2.7/site-packages)
Set PATH to include newly installed bin directory
(e.g. $HOME/opt/bin)

Tune

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Various implementation details

General overview

Trajectories are read frame (single MD timestep) by frame by a trajectory_reader, which is an iterable object. Each frame produced by the trajectory_reader is represented as a dict object containing particle positions etc.

The trajectory_iterator is then wrapped in a "window generator", producing a sliding window of frames through the trajectory. The size of the window is decided by the requested number of time frames to consider for time correlation. The argument --nt determines the width (time length) of the window, and the argument --stride determines how many frames to move the window between two consecutive windows.

Each frame in a window will be processed to have its particle positions (and velocities) split info particle types/species, and for each particle type the corresponding fourier transform of its density (and current) will be calculated. Information about which particle belongs to which type/species comes either from the trajectory file (if available), or from a separate index file (gromacs ndx-style).

For each window, time correlations ranging from delta_t=0 (the "static" correlation) to delta_t= is calculated. The time correlations are averaged over all windows considered.

Each of the averaged time correlations are then further averaged in the reciprocal domain by mapping it into --k-bins values ranging from 0 to --k-max (for an isotropic media, only the absolute of the k-vector is of interest).

For each output format choosen, output is written.

Calls to external libraries

All call to external libraries are used using ctypes. The main reason for choosing ctypes over e.g. swig/weave/f2py/... is portability. Ctypes is included with python, and uses libffi so there is no need for a compiler. A drawback is that lots of API stuff needs to be explicitly set up in python code. If there is a change in the API to the external libraries used, some part of the dynsf code need to be modified.